How Does AI Generate Images? DALL-E, Stable Diffusion, and Midjourney Explained

I'm Parikshet. The first time I typed a description into DALL-E and saw an image appear that matched my words, I genuinely could not believe what I was looking at. How does software turn text into a picture? The answer involves one of the most elegant ideas in modern AI.

The Core Problem

To generate an image from a text description, the AI needs to do two things simultaneously: understand what the words mean visually, and be able to create realistic-looking images. Early AI image systems (like GANs from 2014) could create images but struggled to control them precisely from text. Diffusion models, which became dominant from 2021 onwards, solved both problems together.

How Diffusion Models Work

The training process is beautifully clever. Take a real photograph. Gradually add random noise to it, step by step, until it becomes pure static. Train a neural network to reverse this process — given a slightly noisy image, predict what the slightly less noisy version looks like. Do this for millions of images across thousands of noise levels.

After training, the model has learned the statistical structure of what real images look like. It knows how edges, textures, lighting, and objects relate to each other in photographs.

To generate an image: start with pure random noise. Let the model take a step toward "less noisy, more image-like" — but guided by your text prompt. Repeat ~50 times. Each step produces a slightly clearer image, directed by the words you provided. After 50 steps of guided denoising, you have a new image that never existed before.

The Text Connection: CLIP

The text guidance comes from a model called CLIP (Contrastive Language-Image Pre-training), trained by OpenAI on 400 million image-text pairs from the internet. CLIP learns to map images and text descriptions into the same numerical space — so the word "sunset" maps near images of sunsets. During generation, the diffusion process is steered toward image regions that CLIP scores as matching your text description.

DALL-E vs Midjourney vs Stable Diffusion

DALL-E 3 (OpenAI): Accessible through ChatGPT (free tier generates images). Strong at following specific prompt instructions precisely. Good for accurate representations. Built-in safety filters. Best for: school projects, accurate visualisations.

Midjourney: Produces the most artistically striking images — painterly, atmospheric, highly aesthetic. Operates through Discord. Requires a paid subscription ($10/month+). Best for: artistic, creative work where visual impact matters most.

Stable Diffusion: Open-source, free, runs locally on your computer (if powerful enough) or via free online interfaces. Maximum customisability — thousands of fine-tuned models for specific styles. Higher technical bar. Best for: those who want deep control and no usage limits.

Adobe Firefly: Trained only on licensed Adobe Stock images and public domain art — so images are legally safe for commercial use. Integrated into Photoshop. Best for: professional work where copyright clearance matters.

Writing Better Image Prompts

The difference between "a cat" and a genuinely great AI image is almost entirely in the prompt. My formula:

[Subject] + [Action/state] + [Setting] + [Style] + [Lighting] + [Mood/colour]

Example:
Weak: "a robot"
Strong: "A friendly copper robot sitting in a library reading a book, warm candlelight, illustrated in the style of a 1950s children's book, soft golden tones, peaceful and curious mood"

The more specific your prompt, the more the AI has to work with. Try different styles: "oil painting", "watercolour", "photorealistic", "vector illustration", "Studio Ghibli style", "pencil sketch". Style keywords have an enormous effect on results.